Quantitative Imaging and Analysis Lab

Our keynote talk on Deep Learning Approaches in Spectral CT at the 2nd Annual Translational Imaging Conference AI and Machine Learning in Imaging.

We combined MRI and micro-CT to show that lack of GIT1 results in skull shape abnormalities, brain atrophy, white matter and cortical layer deficiencies. Clustering of volume covariance adjacency matrices identified vulnerable brain networks.

https://www.sciencedirect.com/science/article/pii/S0730725X20304537

Preclinical micro-CT provides a hotbed in which to develop new imaging technologies, including spectral CT using photon counting detector (PCD) technology. Spectral imaging using PCDs promises to expand x-ray CT as a functional imaging modality, capable of molecular imaging, while maintaining CT’s role as a powerful anatomical imaging modality. However, the utility of PCDs suffers due to distorted spectral measurements, affecting the accuracy of material decomposition. We attempt to improve material decomposition accuracy using our novel hybrid dual-source micro-CT system which combines a PCD and an energy integrating detector.  https://doi.org/10.1088/1361-6560/aba8b2… 

Data completion is needed in dual‐source, dual‐energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Here we published a new Deep Learning approach for Spectral Extrapolation!

We have created an image processing pipeline for high-throughput, reduced-bias segmentation of multiparametric tumor MRI data and radiomics analysis, to better our understanding of preclinical imaging and the insights it provides when studying new cancer therapies.

Link to our new paper

Our mission is to develop, optimize and apply novel CT and MRI quantitative imaging at both preclinical and clinical levels !